diff --git a/FESTIM_SurfKin_impl/HTi.ipynb b/FESTIM_SurfKin_impl/HTi.ipynb
deleted file mode 100644
index 044d1c0a2..000000000
--- a/FESTIM_SurfKin_impl/HTi.ipynb
+++ /dev/null
@@ -1,404 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import festim as F\n",
-    "import fenics as f\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "################### PARAMETERS ###################\n",
-    "N_A_const = 6.022e23 # Avogadro, mol^-1\n",
-    "e = 1.602e-19\n",
-    "M_H2 = 2.016e-3 / N_A_const # the H2 mass, kg mol^-1\n",
-    "\n",
-    "# Sample size\n",
-    "A = 1e-2 * 1.3e-2 # Ti surface area (1cm x 1.3cm), m^2\n",
-    "L = 1e-3 / 2 # Ti thickness, m\n",
-    "V = A * L # Ti volume (1cm x 1.3cm x 1mm), m^-3\n",
-    "\n",
-    "# Ti properties\n",
-    "\n",
-    "N_b = 9.4e4 * N_A_const #  the number of atomic sites per unit of volume of Ti, m^-3\n",
-    "N_s = 2.16e-5 * N_A_const # the number of atomic sites per unit of surface area of Ti, m^-2\n",
-    "n_Ti = N_b * V  #  the number of moles of Ti\n",
-    "lambda_Ti = N_s / N_b\n",
-    "\n",
-    "# Properties of fluxes\n",
-    "E_diff = F.kJmol_to_eV(5.3e4 / 1e3) # diffusion activation energy, eV\n",
-    "D0 = 9e-7 # Diffusion pre-factor for D in W, m^2 s^-1\n",
-    "\n",
-    "E_des = F.kJmol_to_eV(1.17e5 / 1e3) # activateion energy for desotpion, eV\n",
-    "k_des = 2.16e8 * N_A_const # frequency factor for the surface desorption\n",
-    "\n",
-    "E_sb = F.kJmol_to_eV(1.36e5 / 1e3) # the activation energy value for the inward subsurface transport, eV\n",
-    "k_sb = 8.56e12 # frequency factor for the inward subsurface transport, s^-1\n",
-    "\n",
-    "E_bs = F.kJmol_to_eV(1.6e5 / 1e3) # the activation energy value for the outward subsurface transport, eV\n",
-    "k_bs = 7.77e13 # frequency factor for the outward subsurface transport, s^-1\n",
-    "\n",
-    "# Chamber\n",
-    "V_ch = 2.95e-3 # the chamber volume, m^-3\n",
-    "P0 = 1.3e4 # the initial pressure, Pa\n",
-    "\n",
-    "################### FUNCTIONS ################### "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Explicit approach"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        1.5e+03 s    Elapsed time so far: 4.0 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        1.5e+03 s    Elapsed time so far: 4.0 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        1.5e+03 s    Elapsed time so far: 3.9 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        1.5e+03 s    Elapsed time so far: 4.0 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        1.5e+03 s    Elapsed time so far: 4.0 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "def S0(T):\n",
-    "    # the capturing coefficient\n",
-    "    return 0.0143 * f.exp(F.kJmol_to_eV(1.99) / F.k_B / T)\n",
-    "\n",
-    "def P_H2(T, X):\n",
-    "    # partial pressure of hydrogen, Pa\n",
-    "    X0 = 0\n",
-    "    return F.k_B * T * e / V_ch * (P0 * V_ch / (F.k_B * T * e) +  (X0 - X) * (2*n_Ti) / 2)\n",
-    "\n",
-    "def J_sb(T, theta, mobile_conc):\n",
-    "    # the hydrogen flux for the inward subsurface transport,  m^-2 s^-1\n",
-    "    return k_sb * N_s * theta * (1-mobile_conc/N_b) * f.exp(-E_sb / F.k_B / T)\n",
-    "\n",
-    "def J_bs(T, theta, mobile_conc):\n",
-    "    # the hydrogen flux for the outward subsurface transport,  m^-2 s^-1\n",
-    "    return k_bs * N_s * (mobile_conc/N_b) * (1-theta) * f.exp(-E_bs / F.k_B / T)\n",
-    "\n",
-    "def theta_eq(T, X):\n",
-    "    alpha = 2 * S0(T) * P_H2(T, X) / k_des / f.exp(-E_des / F.k_B / T) / (2 * np.pi * M_H2 * F.k_B * T * e)**0.5\n",
-    "    return alpha**0.5 / (1 + alpha**0.5)\n",
-    "\n",
-    "def LeftFlux(T, mobile_conc, dmobile_conc, X):\n",
-    "    theta = theta_eq(T, X)\n",
-    "    return -lambda_Ti * dmobile_conc + J_sb(T, theta, mobile_conc) - J_bs(T, theta, mobile_conc)\n",
-    "\n",
-    "################### CUSTOM MODEL CLASS ###################\n",
-    "class CustomSimulation(F.Simulation):\n",
-    "    def iterate(self):\n",
-    "        prev_sol = self.mobile.solution(0)\n",
-    "        super().iterate()\n",
-    "\n",
-    "        # Surface concentration based on flux\n",
-    "        X_old = left_surface_bc.prms[\"X\"]\n",
-    "        surf_conc = theta_eq(self.T.T(0), X_old) * N_s\n",
-    "\n",
-    "        # FLuxes\n",
-    "        X = (f.assemble(self.mobile.solution * self.mesh.dx) + surf_conc) * A / n_Ti\n",
-    "        dmobile_conc = (self.mobile.solution(0) - prev_sol) / float(self.dt.value)\n",
-    "\n",
-    "        # Update surface and subsurface concentrations\n",
-    "        left_surface_bc.prms['dmobile_conc'].assign(dmobile_conc)\n",
-    "        left_surface_bc.prms['X'].assign(X)\n",
-    "        \n",
-    "################### CUSTOM EXPORT CLASS ###################\n",
-    "class Content(F.SurfaceQuantity):\n",
-    "    def __init__(self, surface) -> None:\n",
-    "        super().__init__(field=\"solute\", surface=surface)\n",
-    "\n",
-    "    @property\n",
-    "    def title(self):\n",
-    "        quantity_title = f\"Content\"\n",
-    "        return quantity_title\n",
-    "\n",
-    "    def compute(self):\n",
-    "        return float(left_surface_bc.prms['X'])\n",
-    "    \n",
-    "################### MODEL ###################\n",
-    "for i, T0 in enumerate([450+273, 500+273, 550+273, 600+273, 650+273]):\n",
-    "    Ti_model = CustomSimulation(log_level = 40)\n",
-    "\n",
-    "    # Mesh\n",
-    "    vertices = np.linspace(0, L, num = 251)\n",
-    "    Ti_model.mesh = F.MeshFromVertices(np.sort(vertices))\n",
-    "\n",
-    "    # Materials\n",
-    "    Ti_model.materials = F.Material(id=1, D_0=D0, E_D=E_diff)\n",
-    "\n",
-    "    # Boundary conditions\n",
-    "    left_surface_bc = F.CustomFlux(surfaces=1, field = \"solute\", function = LeftFlux, X=0, dmobile_conc=0)\n",
-    "\n",
-    "    Ti_model.boundary_conditions = [left_surface_bc]\n",
-    "\n",
-    "    # Exports\n",
-    "    n_exp = 10\n",
-    "    results_folder = \"test/\"\n",
-    "\n",
-    "    derived_quantities = [F.DerivedQuantities([F.HydrogenFlux(surface=1), \n",
-    "                                            F.TotalVolume(field='solute', volume=1),\n",
-    "                                            Content(surface = 1)],\n",
-    "                                            nb_iterations_between_compute = 1,\n",
-    "                                            filename=results_folder + f\"derived_quantities_expl_{T0}.csv\")]\n",
-    "\n",
-    "    Ti_model.exports = derived_quantities\n",
-    "\n",
-    "    Ti_model.T = F.Temperature(value=T0)\n",
-    "\n",
-    "    Ti_model.dt = F.Stepsize(\n",
-    "        initial_value=1e-3,\n",
-    "        stepsize_change_ratio=1.1,\n",
-    "        max_stepsize=5,\n",
-    "        dt_min=1e-4\n",
-    "    )\n",
-    "\n",
-    "    Ti_model.settings = F.Settings(\n",
-    "        absolute_tolerance=1e10,\n",
-    "        relative_tolerance=1e-5,\n",
-    "        maximum_iterations=50,\n",
-    "        final_time=25*60,\n",
-    "    )\n",
-    "\n",
-    "    \n",
-    "    Ti_model.initialise()\n",
-    "    Ti_model.run()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Implicit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        1.5e+03 s    Elapsed time so far: 4.2 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        1.5e+03 s    Elapsed time so far: 4.5 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "0.0 %        1.8e-01 s    Elapsed time so far: 0.3 s\r"
-     ]
-    }
-   ],
-   "source": [
-    "def S0(T):\n",
-    "    # the capturing coefficient\n",
-    "    return 0.0143 * f.exp(F.kJmol_to_eV(1.99) / F.k_B / T)\n",
-    "\n",
-    "def P_H2(T, X):\n",
-    "    # partial pressure of hydrogen, Pa\n",
-    "    X0 = 0\n",
-    "    return F.k_B * T * e / V_ch * (P0 * V_ch / (F.k_B * T * e) +  (X0 - X) * (2*n_Ti) / 2)\n",
-    "\n",
-    "def J_vs(surf_conc, T, X):\n",
-    "    J_ads = 2 * S0(T) * (1 - surf_conc / N_s) ** 2 * P_H2(T, X)  / (2*np.pi*M_H2*F.k_B*T*e)**0.5\n",
-    "    J_des = k_des * (surf_conc / N_s) ** 2 * f.exp(-E_des/F.k_B/T)\n",
-    "    return J_ads - J_des\n",
-    "\n",
-    "def LeftFlux(T, mob, X):\n",
-    "    #theta = theta_eq(T, X)\n",
-    "    #return -lambda_Ti * dmobile_conc + J_sb(T, theta, mobile_conc) - J_bs(T, theta, mobile_conc)\n",
-    "    return 0\n",
-    "\n",
-    "################### CUSTOM MODEL CLASS ###################\n",
-    "class CustomSimulation(F.Simulation):\n",
-    "    def iterate(self):\n",
-    "        super().iterate()\n",
-    "\n",
-    "        # Content\n",
-    "        X = (f.assemble(self.mobile.solution * self.mesh.dx)) * A / n_Ti\n",
-    "\n",
-    "        # Update surface and subsurface concentrations\n",
-    "        self.h_transport_problem.surface_species[0].prms[\"X\"].assign(X)\n",
-    "        \n",
-    "################### CUSTOM EXPORT CLASS ###################\n",
-    "class Content(F.SurfaceQuantity):\n",
-    "    def __init__(self, surface) -> None:\n",
-    "        super().__init__(field=\"solute\", surface=surface)\n",
-    "\n",
-    "    @property\n",
-    "    def title(self):\n",
-    "        quantity_title = f\"Content\"\n",
-    "        return quantity_title\n",
-    "\n",
-    "    def compute(self):\n",
-    "        return float(surf_conc.prms['X'])\n",
-    "    \n",
-    "################### MODEL ###################\n",
-    "for i, T0 in enumerate([450+273, 500+273, 550+273, 600+273, 650+273]):\n",
-    "    Ti_model_impl = CustomSimulation(log_level = 40)\n",
-    "\n",
-    "    # Mesh\n",
-    "    vertices = np.linspace(0, L, num = 251)\n",
-    "    Ti_model_impl.mesh = F.MeshFromVertices(np.sort(vertices))\n",
-    "\n",
-    "    # Materials\n",
-    "    Ti_model_impl.materials = F.Material(id=1, D_0=D0, E_D=E_diff)\n",
-    "\n",
-    "    # Boundary conditions\n",
-    "    left_surface_bc = F.CustomFlux(surfaces=1, field = \"solute\", function = LeftFlux, X=0)\n",
-    "\n",
-    "    Ti_model_impl.boundary_conditions = [left_surface_bc]\n",
-    "\n",
-    "    surf_conc = F.SurfaceConcentration(k_sb, E_sb, k_bs, E_bs, lambda_Ti, N_s, N_b, J_vs, 1, X=0)\n",
-    "    Ti_model_impl.surface_concentrations = [surf_conc]\n",
-    "\n",
-    "    # Exports\n",
-    "    n_exp = 10\n",
-    "    results_folder = \"test/\"\n",
-    "\n",
-    "    derived_quantities = [F.DerivedQuantities([F.HydrogenFlux(surface=1), \n",
-    "                                            F.TotalVolume(field='solute', volume=1),\n",
-    "                                            Content(surface = 1)],\n",
-    "                                            nb_iterations_between_compute = 1,\n",
-    "                                            filename=results_folder + f\"derived_quantities_impl_{T0}.csv\")]\n",
-    "\n",
-    "    Ti_model_impl.exports = derived_quantities\n",
-    "\n",
-    "    Ti_model_impl.T = F.Temperature(value=T0)\n",
-    "\n",
-    "    Ti_model_impl.dt = F.Stepsize(\n",
-    "        initial_value=1e-3,\n",
-    "        stepsize_change_ratio=1.1,\n",
-    "        max_stepsize=5,\n",
-    "        dt_min=1e-4\n",
-    "    )\n",
-    "\n",
-    "    Ti_model_impl.settings = F.Settings(\n",
-    "        absolute_tolerance=1e10,\n",
-    "        relative_tolerance=1e-5,\n",
-    "        maximum_iterations=50,\n",
-    "        final_time=25*60,\n",
-    "    )\n",
-    "\n",
-    "    \n",
-    "    Ti_model_impl.initialise()\n",
-    "    Ti_model_impl.run()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "colors = [\"violet\", \"tab:green\", \"tab:blue\", \"tab:olive\", \"tab:red\"]\n",
-    "for i, T0 in enumerate([450+273, 500+273, 550+273, 600+273, 650+273]):\n",
-    "    data_impl = pd.read_csv(f\"./test/derived_quantities_impl_{T0}.csv\", header = 0)\n",
-    "    plt.plot(data_impl[\"t(s)\"]/60, data_impl[\"Content\"], color = colors[i], lw = 2, label = f\"T={T0-273}\"+ r\"$^{\\circ}$C\", ls = 'solid')\n",
-    "\n",
-    "    data_expl = pd.read_csv(f\"./test/derived_quantities_expl_{T0}.csv\", header = 0)\n",
-    "    plt.plot(data_expl[\"t(s)\"]/60, data_expl[\"Content\"], color = colors[i], lw = 4, label = f\"T={T0-273}\"+ r\"$^{\\circ}$C\", ls = 'dashed')\n",
-    "\n",
-    "    exp = pd.read_csv(f\"./exp_data/{T0-273}.csv\", header=None, skiprows = 1, sep=\",\")\n",
-    "    plt.scatter(exp[0], exp[1], edgecolors = colors[i], facecolors='none', marker = '^', s = 75, linewidths=1.2)\n",
-    "\n",
-    "\n",
-    "plt.ylabel('Content, H/Ti', fontsize = 10)\n",
-    "plt.xlabel('t, min', fontsize = 10)\n",
-    "plt.ylim(0,0.8)\n",
-    "plt.xlim(0,25)\n",
-    "plt.yticks(fontsize=10)\n",
-    "plt.xticks(fontsize=10)\n",
-    "plt.legend(fontsize = 8, ncols = 2)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
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diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_impl_723.csv b/FESTIM_SurfKin_impl/test/derived_quantities_impl_723.csv
deleted file mode 100644
index bd0cf74ef..000000000
--- a/FESTIM_SurfKin_impl/test/derived_quantities_impl_723.csv
+++ /dev/null
@@ -1,383 +0,0 @@
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diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_impl_773.csv b/FESTIM_SurfKin_impl/test/derived_quantities_impl_773.csv
deleted file mode 100644
index 7aa1098e8..000000000
--- a/FESTIM_SurfKin_impl/test/derived_quantities_impl_773.csv
+++ /dev/null
@@ -1,383 +0,0 @@
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diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_impl_823.csv b/FESTIM_SurfKin_impl/test/derived_quantities_impl_823.csv
deleted file mode 100644
index 131284a9f..000000000
--- a/FESTIM_SurfKin_impl/test/derived_quantities_impl_823.csv
+++ /dev/null
@@ -1,381 +0,0 @@
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diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_impl_873.csv b/FESTIM_SurfKin_impl/test/derived_quantities_impl_873.csv
deleted file mode 100644
index cadc4ca42..000000000
--- a/FESTIM_SurfKin_impl/test/derived_quantities_impl_873.csv
+++ /dev/null
@@ -1,381 +0,0 @@
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diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_impl_923.csv b/FESTIM_SurfKin_impl/test/derived_quantities_impl_923.csv
deleted file mode 100644
index 54a582df6..000000000
--- a/FESTIM_SurfKin_impl/test/derived_quantities_impl_923.csv
+++ /dev/null
@@ -1,381 +0,0 @@
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diff --git a/Validation/DWO/exp.csv b/Validation/DWO/exp.csv
new file mode 100644
index 000000000..95581b864
--- /dev/null
+++ b/Validation/DWO/exp.csv
@@ -0,0 +1,66 @@
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\ No newline at end of file
diff --git a/Validation/DWO/test.py b/Validation/DWO/test.py
new file mode 100644
index 000000000..baac780b8
--- /dev/null
+++ b/Validation/DWO/test.py
@@ -0,0 +1,187 @@
+import festim as F
+import fenics as f
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import sympy as sp
+import warnings
+
+warnings.filterwarnings("ignore")
+
+################### PARAMETERS ###################
+N_A_const = 6.022e23  # Avogadro, mol^-1
+e = 1.602e-19
+M_D2 = 4.028e-3 / N_A_const  # the H2 mass, kg mol^-1
+
+# Exposure conditions
+P_D2 = 2e-5  # Pa
+T0 = 300  # K
+ramp = 5  # K/s
+t_imp = 3000  # exposure duration, s
+t_storage = 3600  # storage time, s
+t_TDS = 100  # s
+L = 2e-3 / 2  # half thickness, m
+
+# W properties
+rho_W = 6.3382e28  # W atomic concentration, m^-3
+n_IS = 6 * rho_W  # concentration of interstitial sites, m^-3
+n_surf = 1.416e19  # concentration of adsorption sites, m^-2
+lamda_des = 1 / np.sqrt(n_surf)  # average distance between adsorption sites, m
+lambda_abs = n_surf / n_IS  # distance between subsurface IS and adsorption sites, m
+nu0 = 1e13  # attempt frequency, s^-1
+
+D0 = 1.9e-7 / np.sqrt(2)  # diffusivity pre-factor, m^2 s^-1
+E_diff = 0.2  # diffusion activation energy, eV
+
+# Transitions
+E_bs = E_diff  # energy barrier from bulk to surface, eV
+E_diss = 0  # energy barrier for D2 dissociation, eV
+Q_sol = 1  # heat of solution, eV
+
+
+################### FUNCTIONS ###################
+def S0(T):
+    # the capturing coefficient
+    return f.exp(-E_diss / F.k_B / T)
+
+
+def E_des(surf_conc):
+    theta_D = surf_conc / n_surf
+    theta_D0 = 0.253
+    del_theta_D = 0.180
+    E0 = 1.142
+    dE = 0.346
+    alpha = 0.303
+    beta = 8.902
+
+    E_FD = E0 + dE / (1 + f.exp((theta_D - theta_D0) / del_theta_D))
+    E_des = E_FD * (1 - alpha * f.exp(-beta * (1 - theta_D)))
+
+    return E_des
+
+
+def E_sb(surf_conc):
+    # energy barrier from surface to bulk, eV
+    return (E_des(surf_conc) - E_diss) / 2 + E_bs + Q_sol
+
+
+def J_vs(surf_conc, T, t):
+    J_ads = (
+        2
+        * S0(T)
+        * (1 - surf_conc / n_surf) ** 2
+        * f.conditional(t <= t_imp, 1.52e18, 0)
+    )
+
+    J_des = (
+        2 * nu0 * (lamda_des * surf_conc) ** 2 * f.exp(-E_des(surf_conc) / F.k_B / T)
+    )
+    return J_ads - J_des
+
+
+################### MODEL ###################
+W_model = F.Simulation(log_level=20)
+
+# Mesh
+vertices = np.linspace(0, L, num=500)
+W_model.mesh = F.MeshFromVertices(np.sort(vertices))
+
+# Materials
+tungsten = F.Material(id=1, D_0=D0, E_D=E_diff)
+W_model.materials = tungsten
+
+surf_conc = F.SurfaceConcentration(
+    k_sb=nu0,
+    E_sb=E_sb,
+    k_bs=nu0,
+    E_bs=E_bs,
+    l_abs=lambda_abs,
+    N_s=n_surf,
+    N_b=n_IS,
+    J_vs=J_vs,
+    surfaces=1,
+    initial_condition=F.InitialCondition(field="adsorbed", value=0),
+    t=F.t,
+)
+
+W_model.surface_concentrations = [surf_conc]
+
+traps = F.Traps(
+    [
+        F.Trap(
+            k_0=nu0 / n_IS,
+            E_k=E_diff,
+            p_0=nu0,
+            E_p=0.912,
+            density=7.7e-6,
+            materials=tungsten,
+        ),
+        F.Trap(
+            k_0=nu0 / n_IS,
+            E_k=E_diff,
+            p_0=nu0,
+            E_p=1.176,
+            density=7.9e-7,
+            materials=tungsten,
+        ),
+    ]
+)
+# W_model.traps = traps
+
+W_model.T = F.Temperature(
+    value=sp.Piecewise(
+        (T0, F.t <= t_imp + t_storage), (T0 + ramp * (F.t - t_imp - t_storage), True)
+    )
+)
+
+W_model.dt = F.Stepsize(
+    initial_value=1e-7,
+    stepsize_change_ratio=1.25,
+    max_stepsize=lambda t: 10 if t < t_imp + t_storage else 0.1,
+    dt_min=1e-9,
+)
+
+W_model.settings = F.Settings(
+    absolute_tolerance=1e8,
+    relative_tolerance=1e-7,
+    maximum_iterations=50,
+    final_time=t_imp + t_storage + t_TDS,
+)
+
+# Exports
+results_folder = "test/"
+
+derived_quantities = F.DerivedQuantities(
+    [F.AdsorbedHydrogen(surface=1), F.TotalSurface(field="T", surface=1)],
+    nb_iterations_between_compute=1,
+    filename=results_folder + "Clean_W.csv",
+)
+
+W_model.exports = [derived_quantities]
+
+W_model.initialise()
+W_model.run()
+
+results = pd.read_csv(results_folder + "Clean_W.csv", header=0)
+results["Flux"] = results["Concentration of adsorbed H on surface 1"]
+for i in range(len(results)):
+    surf_conc = results["Concentration of adsorbed H on surface 1"][i]
+    T = results["Total T surface 1"][i]
+    results["Flux"][i] = (
+        2 * nu0 * (lamda_des * surf_conc) ** 2 * np.exp(-E_des(surf_conc) / F.k_B / T)
+    )
+
+
+plt.plot(
+    results["Total T surface 1"][results["t(s)"] >= t_imp + t_storage],
+    results["Flux"][results["t(s)"] >= t_imp + t_storage],
+    linewidth=3,
+)
+
+exp = pd.read_csv(f"./exp.csv", header=None, skiprows=1, sep=",")
+plt.scatter(exp[0], exp[1], marker="x", s=75, linewidths=1.2)
+
+plt.ylabel(r"Desorption flux (m$^{-2}$ s$^{-1}$)")
+plt.xlabel(r"Time (s)")
+plt.xlim(300, 800)
+plt.show()
diff --git a/Validation/DWO/test/Clean_W.csv b/Validation/DWO/test/Clean_W.csv
new file mode 100644
index 000000000..71552830f
--- /dev/null
+++ b/Validation/DWO/test/Clean_W.csv
@@ -0,0 +1,1698 @@
+t(s),Concentration of adsorbed H on surface 1,Total T surface 1
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diff --git a/Validation/DWO/test/derived_quantities_expl_300.csv b/Validation/DWO/test/derived_quantities_expl_300.csv
new file mode 100644
index 000000000..86a17ce4c
--- /dev/null
+++ b/Validation/DWO/test/derived_quantities_expl_300.csv
@@ -0,0 +1,1417 @@
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diff --git a/Validation/HTi/HTi.ipynb b/Validation/HTi/HTi.ipynb
new file mode 100644
index 000000000..68f960e95
--- /dev/null
+++ b/Validation/HTi/HTi.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import festim as F\n",
+    "import fenics as f\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "################### PARAMETERS ###################\n",
+    "N_A_const = 6.022e23 # Avogadro, mol^-1\n",
+    "e = 1.602e-19\n",
+    "M_H2 = 2.016e-3 / N_A_const # the H2 mass, kg mol^-1\n",
+    "\n",
+    "# Sample size\n",
+    "A = 1e-2 * 1.3e-2 # Ti surface area (1cm x 1.3cm), m^2\n",
+    "L = 1e-3 / 2 # Ti thickness, m\n",
+    "V = A * L # Ti volume (1cm x 1.3cm x 1mm), m^-3\n",
+    "\n",
+    "# Ti properties\n",
+    "\n",
+    "N_b = 9.4e4 * N_A_const #  the number of atomic sites per unit of volume of Ti, m^-3\n",
+    "N_s = 2.16e-5 * N_A_const # the number of atomic sites per unit of surface area of Ti, m^-2\n",
+    "n_Ti = N_b * V  #  the number of moles of Ti\n",
+    "lambda_Ti = N_s / N_b\n",
+    "\n",
+    "# Properties of fluxes\n",
+    "E_diff = F.kJmol_to_eV(5.3e4 / 1e3) # diffusion activation energy, eV\n",
+    "D0 = 9e-7 # Diffusion pre-factor for D in W, m^2 s^-1\n",
+    "\n",
+    "E_des = F.kJmol_to_eV(1.17e5 / 1e3) # activateion energy for desotpion, eV\n",
+    "k_des = 2.16e8 * N_A_const # frequency factor for the surface desorption\n",
+    "\n",
+    "E_sb = F.kJmol_to_eV(1.36e5 / 1e3) # the activation energy value for the inward subsurface transport, eV\n",
+    "k_sb = 8.56e12 # frequency factor for the inward subsurface transport, s^-1\n",
+    "\n",
+    "E_bs = F.kJmol_to_eV(1.6e5 / 1e3) # the activation energy value for the outward subsurface transport, eV\n",
+    "k_bs = 7.77e13 # frequency factor for the outward subsurface transport, s^-1\n",
+    "\n",
+    "# Chamber\n",
+    "V_ch = 2.95e-3 # the chamber volume, m^-3\n",
+    "P0 = 1.3e4 # the initial pressure, Pa\n",
+    "\n",
+    "################### FUNCTIONS ################### "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Explicit approach"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 3.8 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 3.6 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 3.5 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 3.6 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 3.5 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "def S0(T):\n",
+    "    # the capturing coefficient\n",
+    "    return 0.0143 * f.exp(F.kJmol_to_eV(1.99) / F.k_B / T)\n",
+    "\n",
+    "def P_H2(T, X):\n",
+    "    # partial pressure of hydrogen, Pa\n",
+    "    X0 = 0\n",
+    "    return F.k_B * T * e / V_ch * (P0 * V_ch / (F.k_B * T * e) +  (X0 - X) * (2*n_Ti) / 2)\n",
+    "\n",
+    "def J_sb(T, theta, mobile_conc):\n",
+    "    # the hydrogen flux for the inward subsurface transport,  m^-2 s^-1\n",
+    "    return k_sb * N_s * theta * (1-mobile_conc/N_b) * f.exp(-E_sb / F.k_B / T)\n",
+    "\n",
+    "def J_bs(T, theta, mobile_conc):\n",
+    "    # the hydrogen flux for the outward subsurface transport,  m^-2 s^-1\n",
+    "    return k_bs * N_s * (mobile_conc/N_b) * (1-theta) * f.exp(-E_bs / F.k_B / T)\n",
+    "\n",
+    "def theta_eq(T, X):\n",
+    "    alpha = 2 * S0(T) * P_H2(T, X) / k_des / f.exp(-E_des / F.k_B / T) / (2 * np.pi * M_H2 * F.k_B * T * e)**0.5\n",
+    "    return alpha**0.5 / (1 + alpha**0.5)\n",
+    "\n",
+    "def LeftFlux(T, mobile_conc, dmobile_conc, X):\n",
+    "    theta = theta_eq(T, X)\n",
+    "    return -lambda_Ti * dmobile_conc + J_sb(T, theta, mobile_conc) - J_bs(T, theta, mobile_conc)\n",
+    "\n",
+    "################### CUSTOM MODEL CLASS ###################\n",
+    "class CustomSimulation(F.Simulation):\n",
+    "    def iterate(self):\n",
+    "        prev_sol = self.mobile.solution(0)\n",
+    "        super().iterate()\n",
+    "\n",
+    "        # Surface concentration based on flux\n",
+    "        X_old = left_surface_bc.prms[\"X\"]\n",
+    "        surf_conc = theta_eq(self.T.T(0), X_old) * N_s\n",
+    "\n",
+    "        # FLuxes\n",
+    "        X = (f.assemble(self.mobile.solution * self.mesh.dx) + surf_conc) * A / n_Ti\n",
+    "        dmobile_conc = (self.mobile.solution(0) - prev_sol) / float(self.dt.value)\n",
+    "\n",
+    "        # Update surface and subsurface concentrations\n",
+    "        left_surface_bc.prms['dmobile_conc'].assign(dmobile_conc)\n",
+    "        left_surface_bc.prms['X'].assign(X)\n",
+    "        \n",
+    "################### CUSTOM EXPORT CLASS ###################\n",
+    "class Content(F.SurfaceQuantity):\n",
+    "    def __init__(self, surface) -> None:\n",
+    "        super().__init__(field=\"solute\", surface=surface)\n",
+    "\n",
+    "    @property\n",
+    "    def title(self):\n",
+    "        quantity_title = f\"Content\"\n",
+    "        return quantity_title\n",
+    "\n",
+    "    def compute(self):\n",
+    "        return float(left_surface_bc.prms['X'])\n",
+    "    \n",
+    "################### MODEL ###################\n",
+    "for i, T0 in enumerate([450+273, 500+273, 550+273, 600+273, 650+273]):\n",
+    "    Ti_model = CustomSimulation(log_level = 40)\n",
+    "\n",
+    "    # Mesh\n",
+    "    vertices = np.linspace(0, L, num = 251)\n",
+    "    Ti_model.mesh = F.MeshFromVertices(np.sort(vertices))\n",
+    "\n",
+    "    # Materials\n",
+    "    Ti_model.materials = F.Material(id=1, D_0=D0, E_D=E_diff)\n",
+    "\n",
+    "    # Boundary conditions\n",
+    "    left_surface_bc = F.CustomFlux(surfaces=1, field = \"solute\", function = LeftFlux, X=0, dmobile_conc=0)\n",
+    "\n",
+    "    Ti_model.boundary_conditions = [left_surface_bc]\n",
+    "\n",
+    "    # Exports\n",
+    "    n_exp = 10\n",
+    "    results_folder = \"test/\"\n",
+    "\n",
+    "    derived_quantities = [F.DerivedQuantities([F.HydrogenFlux(surface=1), \n",
+    "                                            F.TotalVolume(field='solute', volume=1),\n",
+    "                                            Content(surface = 1)],\n",
+    "                                            nb_iterations_between_compute = 1,\n",
+    "                                            filename=results_folder + f\"derived_quantities_expl_{T0}.csv\")]\n",
+    "\n",
+    "    Ti_model.exports = derived_quantities\n",
+    "\n",
+    "    Ti_model.T = F.Temperature(value=T0)\n",
+    "\n",
+    "    Ti_model.dt = F.Stepsize(\n",
+    "        initial_value=1e-3,\n",
+    "        stepsize_change_ratio=1.1,\n",
+    "        max_stepsize=5,\n",
+    "        dt_min=1e-4\n",
+    "    )\n",
+    "\n",
+    "    Ti_model.settings = F.Settings(\n",
+    "        absolute_tolerance=1e10,\n",
+    "        relative_tolerance=1e-5,\n",
+    "        maximum_iterations=50,\n",
+    "        final_time=25*60,\n",
+    "    )\n",
+    "\n",
+    "    \n",
+    "    Ti_model.initialise()\n",
+    "    Ti_model.run()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Implicit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 4.0 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 4.0 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 4.4 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 4.0 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        1.5e+03 s    Elapsed time so far: 3.8 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "def S0(T):\n",
+    "    # the capturing coefficient\n",
+    "    return 0.0143 * f.exp(F.kJmol_to_eV(1.99) / F.k_B / T)\n",
+    "\n",
+    "def P_H2(T, X):\n",
+    "    # partial pressure of hydrogen, Pa\n",
+    "    X0 = 0\n",
+    "    return F.k_B * T * e / V_ch * (P0 * V_ch / (F.k_B * T * e) +  (X0 - X) * (2*n_Ti) / 2)\n",
+    "\n",
+    "def J_vs(surf_conc, T, X):\n",
+    "    J_ads = 2 * S0(T) * (1 - surf_conc / N_s) ** 2 * P_H2(T, X)  / (2*np.pi*M_H2*F.k_B*T*e)**0.5\n",
+    "    J_des = k_des * (surf_conc / N_s) ** 2 * f.exp(-E_des/F.k_B/T)\n",
+    "    return J_ads - J_des\n",
+    "\n",
+    "################### CUSTOM MODEL CLASS ###################\n",
+    "class CustomSimulation(F.Simulation):\n",
+    "    def iterate(self):\n",
+    "        super().iterate()\n",
+    "\n",
+    "        # Content\n",
+    "        X = (f.assemble(self.mobile.solution * self.mesh.dx)) * A / n_Ti\n",
+    "\n",
+    "        # Update surface and subsurface concentrations\n",
+    "        self.h_transport_problem.surface_concentrations[0].prms[\"X\"].assign(X)\n",
+    "    \n",
+    "################### MODEL ###################\n",
+    "for i, T0 in enumerate([450+273, 500+273, 550+273, 600+273, 650+273]):\n",
+    "    Ti_model_impl = CustomSimulation(log_level = 40)\n",
+    "\n",
+    "    # Mesh\n",
+    "    vertices = np.linspace(0, L, num = 251)\n",
+    "    Ti_model_impl.mesh = F.MeshFromVertices(np.sort(vertices))\n",
+    "\n",
+    "    # Materials\n",
+    "    Ti_model_impl.materials = F.Material(id=1, D_0=D0, E_D=E_diff)\n",
+    "\n",
+    "    surf_conc = F.SurfaceConcentration(k_sb, E_sb, k_bs, E_bs, lambda_Ti, N_s, N_b, J_vs, 1, F.InitialCondition(\"adsorbed\", 0),X=0)\n",
+    "    Ti_model_impl.surface_concentrations = [surf_conc]\n",
+    "\n",
+    "    # Exports\n",
+    "    n_exp = 10\n",
+    "    results_folder = \"test/\"\n",
+    "\n",
+    "    derived_quantities = [F.DerivedQuantities([ \n",
+    "                                                F.AdsorbedHydrogen(surface=1),\n",
+    "                                                F.TotalVolume(field=\"solute\", volume = 1)],\n",
+    "                                                nb_iterations_between_compute = 1,\n",
+    "                                                filename=results_folder + f\"derived_quantities_impl_{T0}.csv\")]\n",
+    "\n",
+    "    Ti_model_impl.exports = derived_quantities\n",
+    "\n",
+    "    Ti_model_impl.T = F.Temperature(value=T0)\n",
+    "\n",
+    "    Ti_model_impl.dt = F.Stepsize(\n",
+    "        initial_value=1e-3,\n",
+    "        stepsize_change_ratio=1.1,\n",
+    "        max_stepsize=5,\n",
+    "        dt_min=1e-4\n",
+    "    )\n",
+    "\n",
+    "    Ti_model_impl.settings = F.Settings(\n",
+    "        absolute_tolerance=1e10,\n",
+    "        relative_tolerance=1e-5,\n",
+    "        maximum_iterations=50,\n",
+    "        final_time=25*60,\n",
+    "    )\n",
+    "\n",
+    "    \n",
+    "    Ti_model_impl.initialise()\n",
+    "    Ti_model_impl.run()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = [\"violet\", \"tab:green\", \"tab:blue\", \"tab:olive\", \"tab:red\"]\n",
+    "for i, T0 in enumerate([450+273, 500+273, 550+273, 600+273, 650+273]):\n",
+    "    data_impl = pd.read_csv(f\"./test/derived_quantities_impl_{T0}.csv\", header = 0)\n",
+    "    plt.plot(data_impl[\"t(s)\"]/60, data_impl[\"Total solute volume 1\"] * A / n_Ti, color = colors[i], lw = 2, label = f\"impl. T={T0-273}\"+ r\"$^{\\circ}$C\", ls = 'solid')\n",
+    "\n",
+    "    data_expl = pd.read_csv(f\"./test/derived_quantities_expl_{T0}.csv\", header = 0)\n",
+    "    plt.plot(data_expl[\"t(s)\"]/60, data_expl[\"Total solute volume 1\"] * A / n_Ti, color = colors[i], lw = 4, label = f\"expl. T={T0-273}\"+ r\"$^{\\circ}$C\", ls = 'dashed')\n",
+    "\n",
+    "    exp = pd.read_csv(f\"./exp_data/{T0-273}.csv\", header=None, skiprows = 1, sep=\",\")\n",
+    "    plt.scatter(exp[0], exp[1], edgecolors = colors[i], facecolors='none', marker = '^', s = 75, linewidths=1.2)\n",
+    "\n",
+    "\n",
+    "plt.ylabel('Content, H/Ti', fontsize = 10)\n",
+    "plt.xlabel('t, min', fontsize = 10)\n",
+    "plt.ylim(0,0.8)\n",
+    "plt.xlim(0,25)\n",
+    "plt.yticks(fontsize=10)\n",
+    "plt.xticks(fontsize=10)\n",
+    "plt.legend(fontsize = 8, ncols = 2)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "festim-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/FESTIM_SurfKin_impl/exp_data/450.csv b/Validation/HTi/exp_data/450.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/exp_data/450.csv
rename to Validation/HTi/exp_data/450.csv
diff --git a/FESTIM_SurfKin_impl/exp_data/500.csv b/Validation/HTi/exp_data/500.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/exp_data/500.csv
rename to Validation/HTi/exp_data/500.csv
diff --git a/FESTIM_SurfKin_impl/exp_data/550.csv b/Validation/HTi/exp_data/550.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/exp_data/550.csv
rename to Validation/HTi/exp_data/550.csv
diff --git a/FESTIM_SurfKin_impl/exp_data/600.csv b/Validation/HTi/exp_data/600.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/exp_data/600.csv
rename to Validation/HTi/exp_data/600.csv
diff --git a/FESTIM_SurfKin_impl/exp_data/650.csv b/Validation/HTi/exp_data/650.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/exp_data/650.csv
rename to Validation/HTi/exp_data/650.csv
diff --git a/Validation/HTi/test.py b/Validation/HTi/test.py
new file mode 100644
index 000000000..e9960fb6d
--- /dev/null
+++ b/Validation/HTi/test.py
@@ -0,0 +1,189 @@
+import festim as F
+import fenics as f
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import warnings
+
+warnings.filterwarnings("ignore")
+
+################### PARAMETERS ###################
+N_A_const = 6.022e23  # Avogadro, mol^-1
+e = 1.602e-19
+M_H2 = 2.016e-3 / N_A_const  # the H2 mass, kg mol^-1
+
+# Sample size
+A = 1e-2 * 1.3e-2  # Ti surface area (1cm x 1.3cm), m^2
+L = 1e-3  # Ti thickness, m
+V = A * L  # Ti volume (1cm x 1.3cm x 1mm), m^-3
+
+# Ti properties
+
+N_b = 9.4e4 * N_A_const  #  the number of atomic sites per unit of volume of Ti, m^-3
+N_s = (
+    2.16e-5 * N_A_const
+)  # the number of atomic sites per unit of surface area of Ti, m^-2
+n_Ti = N_b * V  #  the number of moles of Ti
+lambda_Ti = N_s / N_b
+
+# Properties of fluxes
+E_diff = F.kJmol_to_eV(5.3e4 / 1e3)  # diffusion activation energy, eV
+D0 = 9e-7  # Diffusion pre-factor for D in W, m^2 s^-1
+
+E_des = F.kJmol_to_eV(1.17e5 / 1e3)  # activateion energy for desotpion, eV
+k_des = 2.16e8 * N_A_const  # frequency factor for the surface desorption
+
+E_sb = F.kJmol_to_eV(
+    1.36e5 / 1e3
+)  # the activation energy value for the inward subsurface transport, eV
+k_sb = 8.56e12  # frequency factor for the inward subsurface transport, s^-1
+
+E_bs = F.kJmol_to_eV(
+    1.6e5 / 1e3
+)  # the activation energy value for the outward subsurface transport, eV
+k_bs = 7.77e13  # frequency factor for the outward subsurface transport, s^-1
+
+# Chamber
+V_ch = 2.95e-3  # the chamber volume, m^-3
+P0 = 1.3e4  # the initial pressure, Pa
+
+################### FUNCTIONS ###################
+
+
+def S0(T):
+    # the capturing coefficient
+    return 0.0143 * f.exp(F.kJmol_to_eV(1.99) / F.k_B / T)
+
+
+def P_H2(T, X):
+    # partial pressure of hydrogen, Pa
+    X0 = 0
+    return (
+        F.k_B * T * e / V_ch * (P0 * V_ch / (F.k_B * T * e) + (X0 - X) * (2 * n_Ti) / 2)
+    )
+
+
+def J_vs(surf_conc, T, X):
+    J_ads = (
+        2
+        * S0(T)
+        * (1 - surf_conc / N_s) ** 2
+        * P_H2(T, X)
+        / (2 * np.pi * M_H2 * F.k_B * T * e) ** 0.5
+    )
+    J_des = k_des * (surf_conc / N_s) ** 2 * f.exp(-E_des / F.k_B / T)
+    return J_ads - J_des
+
+
+def J_vs1(surf_conc, T, X):
+    J_ads = (
+        2
+        * S0(T)
+        * (1 - surf_conc / N_s) ** 2
+        * P_H2(T, X)
+        / (2 * np.pi * M_H2 * F.k_B * T * e) ** 0.5
+    )
+    J_des = k_des * (surf_conc / N_s) ** 2 * f.exp(-E_des / F.k_B / T)
+    return J_ads / 100 - J_des
+
+
+################### CUSTOM MODEL CLASS ###################
+class CustomSimulation(F.Simulation):
+    def iterate(self):
+        super().iterate()
+
+        # Content
+        X = (f.assemble(self.mobile.solution * self.mesh.dx)) * A / n_Ti
+
+        # Update surface and subsurface concentrations
+        self.h_transport_problem.surface_concentrations[0].prms["X"].assign(X)
+        self.h_transport_problem.surface_concentrations[1].prms["X"].assign(X)
+
+
+################### MODEL ###################
+for i, T0 in enumerate([450 + 273, 500 + 273, 550 + 273, 600 + 273, 650 + 273]):
+    Ti_model_impl = CustomSimulation(log_level=40)
+
+    # Mesh
+    vertices = np.linspace(0, L, num=251)
+    Ti_model_impl.mesh = F.MeshFromVertices(np.sort(vertices))
+
+    # Materials
+    Ti_model_impl.materials = F.Material(id=1, D_0=D0, E_D=E_diff)
+
+    surf_conc = F.SurfaceConcentration(
+        k_sb=k_sb,
+        E_sb=E_sb,
+        k_bs=k_bs,
+        E_bs=E_bs,
+        l_abs=lambda_Ti,
+        N_s=N_s,
+        N_b=N_b,
+        J_vs=J_vs,
+        surfaces=1,
+        initial_condition=F.InitialCondition(field="adsorbed", value=0),
+        X=0,
+    )
+
+    surf_conc1 = F.SurfaceConcentration(
+        k_sb=k_sb,
+        E_sb=E_sb,
+        k_bs=k_bs,
+        E_bs=E_bs,
+        l_abs=lambda_Ti,
+        N_s=N_s,
+        N_b=N_b,
+        J_vs=J_vs1,
+        surfaces=2,
+        initial_condition=F.InitialCondition(field="adsorbed", value=0),
+        X=0,
+    )
+
+    Ti_model_impl.surface_concentrations = [surf_conc, surf_conc1]
+
+    Ti_model_impl.T = F.Temperature(value=T0)
+
+    Ti_model_impl.dt = F.Stepsize(
+        initial_value=1e-9, stepsize_change_ratio=1.1, max_stepsize=5, dt_min=1e-4
+    )
+
+    Ti_model_impl.settings = F.Settings(
+        absolute_tolerance=1e11,
+        relative_tolerance=1e-5,
+        maximum_iterations=50,
+        final_time=25 * 60,
+    )
+
+    # Exports
+    n_exp = 10
+    results_folder = "test/"
+
+    derived_quantities = [
+        F.DerivedQuantities(
+            [
+                F.TotalVolume(field="solute", volume=1),
+                F.AdsorbedHydrogen(surface=1),
+                F.AdsorbedHydrogen(surface=2),
+            ],
+            nb_iterations_between_compute=1,
+            filename=results_folder + f"derived_quantities_expl_{T0}.csv",
+        )
+    ]
+
+    Ti_model_impl.exports = derived_quantities
+
+    Ti_model_impl.initialise()
+    Ti_model_impl.run()
+
+    data_impl = pd.read_csv(f"./test/derived_quantities_expl_{T0}.csv", header=0)
+    for i in range(1, 3):
+        plt.plot(
+            data_impl["t(s)"] / 60,
+            data_impl[f"Concentration of adsorbed H on surface {i}"] / N_s,
+        )
+
+    plt.xscale("log")
+    plt.show()
+
+    f.plot(Ti_model_impl.h_transport_problem.mobile.solution)
+    plt.show()
diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_expl_723.csv b/Validation/HTi/test/derived_quantities_expl_723.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/test/derived_quantities_expl_723.csv
rename to Validation/HTi/test/derived_quantities_expl_723.csv
diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_expl_773.csv b/Validation/HTi/test/derived_quantities_expl_773.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/test/derived_quantities_expl_773.csv
rename to Validation/HTi/test/derived_quantities_expl_773.csv
diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_expl_823.csv b/Validation/HTi/test/derived_quantities_expl_823.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/test/derived_quantities_expl_823.csv
rename to Validation/HTi/test/derived_quantities_expl_823.csv
diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_expl_873.csv b/Validation/HTi/test/derived_quantities_expl_873.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/test/derived_quantities_expl_873.csv
rename to Validation/HTi/test/derived_quantities_expl_873.csv
diff --git a/FESTIM_SurfKin_impl/test/derived_quantities_expl_923.csv b/Validation/HTi/test/derived_quantities_expl_923.csv
similarity index 100%
rename from FESTIM_SurfKin_impl/test/derived_quantities_expl_923.csv
rename to Validation/HTi/test/derived_quantities_expl_923.csv
diff --git a/Validation/HTi/test/derived_quantities_impl_723.csv b/Validation/HTi/test/derived_quantities_impl_723.csv
new file mode 100644
index 000000000..ea0ae1778
--- /dev/null
+++ b/Validation/HTi/test/derived_quantities_impl_723.csv
@@ -0,0 +1,383 @@
+t(s),Concentration of adsorbed H on surface 1,Total solute volume 1
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diff --git a/Validation/HTi/test/derived_quantities_impl_773.csv b/Validation/HTi/test/derived_quantities_impl_773.csv
new file mode 100644
index 000000000..bba79c195
--- /dev/null
+++ b/Validation/HTi/test/derived_quantities_impl_773.csv
@@ -0,0 +1,383 @@
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diff --git a/Validation/HTi/test/derived_quantities_impl_823.csv b/Validation/HTi/test/derived_quantities_impl_823.csv
new file mode 100644
index 000000000..a5c890d6e
--- /dev/null
+++ b/Validation/HTi/test/derived_quantities_impl_823.csv
@@ -0,0 +1,381 @@
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diff --git a/Validation/HTi/test/derived_quantities_impl_873.csv b/Validation/HTi/test/derived_quantities_impl_873.csv
new file mode 100644
index 000000000..cc848e489
--- /dev/null
+++ b/Validation/HTi/test/derived_quantities_impl_873.csv
@@ -0,0 +1,381 @@
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diff --git a/Validation/HTi/test/derived_quantities_impl_923.csv b/Validation/HTi/test/derived_quantities_impl_923.csv
new file mode 100644
index 000000000..5f1dfb7ca
--- /dev/null
+++ b/Validation/HTi/test/derived_quantities_impl_923.csv
@@ -0,0 +1,381 @@
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diff --git a/festim/__init__.py b/festim/__init__.py
index 62cc07734..90505507e 100644
--- a/festim/__init__.py
+++ b/festim/__init__.py
@@ -84,6 +84,7 @@
 from .exports.derived_quantities.total_volume import TotalVolume
 from .exports.derived_quantities.average_surface import AverageSurface
 from .exports.derived_quantities.point_value import PointValue
+from .exports.derived_quantities.adsorbed_hydrogen import AdsorbedHydrogen
 
 from .exports.derived_quantities.derived_quantities import DerivedQuantities
 
diff --git a/festim/concentration/mobile.py b/festim/concentration/mobile.py
index 6aa1aa947..21365dc90 100644
--- a/festim/concentration/mobile.py
+++ b/festim/concentration/mobile.py
@@ -22,7 +22,14 @@ def __init__(self):
         self.boundary_conditions = []
 
     def create_form(
-        self, materials, mesh, T, dt=None, traps=None, soret=False, surface_species=None
+        self,
+        materials,
+        mesh,
+        T,
+        dt=None,
+        traps=None,
+        soret=False,
+        surface_concentrations=None,
     ):
         """Creates the variational formulation.
 
@@ -40,7 +47,9 @@ def create_form(
         self.F = 0
         self.create_diffusion_form(materials, mesh, T, dt=dt, traps=traps, soret=soret)
         self.create_source_form(mesh.dx)
-        self.create_fluxes_form(T, mesh.ds, dt=dt, surface_species=surface_species)
+        self.create_fluxes_form(
+            T, mesh.ds, dt=dt, surface_concentrations=surface_concentrations
+        )
 
     def create_diffusion_form(
         self, materials, mesh, T, dt=None, traps=None, soret=False
@@ -173,7 +182,7 @@ def create_source_form(self, dx):
         self.F += F_source
         self.sub_expressions += expressions_source
 
-    def create_fluxes_form(self, T, ds, surface_species=None, dt=None):
+    def create_fluxes_form(self, T, ds, surface_concentrations=None, dt=None):
         """Modifies the formulation and adds fluxes based
         on parameters in self.boundary_conditions
         """
@@ -194,8 +203,8 @@ def create_fluxes_form(self, T, ds, surface_species=None, dt=None):
                     for surf in bc.surfaces:
                         F += -self.test_function * bc.form * ds(surf)
 
-        if surface_species:
-            for surf_conc in surface_species:
+        if surface_concentrations:
+            for surf_conc in surface_concentrations:
                 k_sb = surf_conc.k_sb
                 E_sb = surf_conc.E_sb
                 k_bs = surf_conc.k_bs
@@ -205,9 +214,9 @@ def create_fluxes_form(self, T, ds, surface_species=None, dt=None):
                 N_b = surf_conc.N_b
 
                 if callable(E_sb):
-                    E_sb = E_sb(surf_conc.solution, solute)
+                    E_sb = E_sb(surf_conc.solution)
                 if callable(E_bs):
-                    E_bs = E_bs(surf_conc.solution, solute)
+                    E_bs = E_bs(surf_conc.solution)
 
                 J_sb = (
                     k_sb
diff --git a/festim/concentration/surface_species/surface_concentration.py b/festim/concentration/surface_species/surface_concentration.py
index 51c0155d6..05733f415 100644
--- a/festim/concentration/surface_species/surface_concentration.py
+++ b/festim/concentration/surface_species/surface_concentration.py
@@ -6,7 +6,20 @@
 class SurfaceConcentration(Concentration):
     """ """
 
-    def __init__(self, k_sb, E_sb, k_bs, E_bs, l_abs, N_s, N_b, J_vs, surfaces, **prms):
+    def __init__(
+        self,
+        k_sb,
+        E_sb,
+        k_bs,
+        E_bs,
+        l_abs,
+        N_s,
+        N_b,
+        J_vs,
+        surfaces,
+        initial_condition,
+        **prms
+    ):
         super().__init__()
         self.k_sb = k_sb
         self.E_sb = E_sb
@@ -20,6 +33,7 @@ def __init__(self, k_sb, E_sb, k_bs, E_bs, l_abs, N_s, N_b, J_vs, surfaces, **pr
             surfaces = [surfaces]
         self.J_vs = J_vs
         self.surfaces = surfaces
+        self.initial_condition = initial_condition
         self.prms = prms
         self.convert_prms()
 
@@ -42,9 +56,9 @@ def create_form(self, mobile, T, ds, dt):
         if callable(J_vs):
             J_vs = J_vs(solution, T.T, **self.prms)
         if callable(E_sb):
-            E_sb = E_sb(solution, solute)
+            E_sb = E_sb(solution)
         if callable(E_bs):
-            E_bs = E_bs(solution, solute)
+            E_bs = E_bs(solution)
 
         J_sb = k_sb * solution * (1 - solute / N_b) * exp(-E_sb / k_B / T.T)
         J_bs = k_bs * (solute * l_abs) * (1 - solution / N_s) * exp(-E_bs / k_B / T.T)
diff --git a/festim/exports/derived_quantities/adsorbed_hydrogen.py b/festim/exports/derived_quantities/adsorbed_hydrogen.py
new file mode 100644
index 000000000..cf0b7c7ba
--- /dev/null
+++ b/festim/exports/derived_quantities/adsorbed_hydrogen.py
@@ -0,0 +1,39 @@
+from festim import SurfaceQuantity, k_B
+import fenics as f
+
+
+class AdsorbedHydrogen(SurfaceQuantity):
+    """
+    Computes the hydrogen surface concentration on surface
+
+    Args:
+        surface (int): the surface id
+
+    Attribtutes
+        surface (int): the surface id
+        export_unit (str): the unit of the derived quantity in the export file
+        title (str): the title of the derived quantity
+        show_units (bool): show the units in the title in the derived quantities
+            file
+        function (dolfin.function.function.Function): the solution function of
+            the field
+
+    """
+
+    def __init__(self, surface) -> None:
+        super().__init__(field="adsorbed", surface=surface)
+
+    @property
+    def export_unit(self):
+        return f"H m-2"
+
+    @property
+    def title(self):
+        quantity_title = f"Concentration of adsorbed H on surface {self.surface}"
+        if self.show_units:
+            return quantity_title + f" ({self.export_unit})"
+        else:
+            return quantity_title
+
+    def compute(self):
+        return f.assemble(self.function * self.ds(self.surface))
diff --git a/festim/exports/exports.py b/festim/exports/exports.py
index b79cd2f2b..57723ee3d 100644
--- a/festim/exports/exports.py
+++ b/festim/exports/exports.py
@@ -93,7 +93,12 @@ def write(self, label_to_function, dx):
                                 label_to_function[quantity.field] = f.project(
                                     label_to_function[quantity.field], self.V_DG1
                                 )
-                        quantity.function = label_to_function[quantity.field]
+                        if isinstance(quantity, festim.AdsorbedHydrogen):
+                            for surf_funcs in label_to_function[quantity.field]:
+                                if quantity.surface in surf_funcs[1]:
+                                    quantity.function = surf_funcs[0]
+                        else:
+                            quantity.function = label_to_function[quantity.field]
                     export.compute(self.t)
                 # export derived quantities
                 if export.is_export(self.t, self.final_time, self.nb_iterations):
diff --git a/festim/generic_simulation.py b/festim/generic_simulation.py
index 09874b1b7..9ebf70d8c 100644
--- a/festim/generic_simulation.py
+++ b/festim/generic_simulation.py
@@ -257,6 +257,15 @@ def attribute_boundary_conditions(self):
                     raise ValueError(
                         f"A DirichletBC is simultaneously set with another boundary condition on surfaces {intersection} for field {dc_bc.field}"
                     )
+            # checks that surface_concentration is not set with another bc for solute on the same surface
+            for surf_conc in self.surface_concentrations:
+                if bc.field != "T":
+                    if not set(bc.surfaces).isdisjoint(surf_conc.surfaces):
+                        # convert lists of surfaces to sets and obtain their intersection
+                        intersection = set(bc.surfaces) & set(surf_conc.surfaces)
+                        raise ValueError(
+                            f"SurfaceConcentration is simultaneously set with another boundary condition on surfaces {intersection}"
+                        )
 
     def initialise(self):
         """Initialise the model. Defines markers, create the suitable function
@@ -481,6 +490,10 @@ def update_post_processing_solutions(self):
             "solute": self.mobile.post_processing_solution,
             "0": self.mobile.post_processing_solution,
             0: self.mobile.post_processing_solution,
+            "adsorbed": [
+                (surf_conc.post_processing_solution, surf_conc.surfaces)
+                for surf_conc in self.surface_concentrations
+            ],
             "T": self.T.T,
             "retention": sum(
                 [self.mobile.post_processing_solution]
diff --git a/festim/h_transport_problem.py b/festim/h_transport_problem.py
index 9ca950b83..8aaff56c3 100644
--- a/festim/h_transport_problem.py
+++ b/festim/h_transport_problem.py
@@ -26,11 +26,11 @@ class HTransportProblem:
     """
 
     def __init__(
-        self, mobile, traps, T, settings, initial_conditions, surface_species
+        self, mobile, traps, T, settings, initial_conditions, surface_concentrations
     ) -> None:
         self.mobile = mobile
         self.traps = traps
-        self.surface_species = surface_species
+        self.surface_concentrations = surface_concentrations
         self.T = T
         self.settings = settings
         self.initial_conditions = initial_conditions
@@ -92,7 +92,7 @@ def define_function_space(self, mesh):
 
         # function space for H concentrations
         nb_traps = len(self.traps)
-        nb_adsorbed = len(self.surface_species)
+        nb_adsorbed = len(self.surface_concentrations)
 
         if nb_traps == 0 and nb_adsorbed == 0:
             V = FunctionSpace(mesh.mesh, element_solute, order_solute)
@@ -136,8 +136,8 @@ def initialise_concentrations(self):
             conc_list = [self.mobile]
             if self.traps:
                 conc_list += [*self.traps]
-            if self.surface_species:
-                conc_list += [*self.surface_species]
+            if self.surface_concentrations:
+                conc_list += [*self.surface_concentrations]
 
             for i, concentration in enumerate(conc_list):
                 concentration.solution = self.u.sub(i)
@@ -174,6 +174,19 @@ def initialise_concentrations(self):
                     functionspace, value, label=ini.label, time_step=ini.time_step
                 )
 
+        if self.surface_concentrations:
+            for i, surf_conc in enumerate(
+                self.surface_concentrations, len(self.traps) + 1
+            ):
+                functionspace = self.V.sub(i).collapse()
+                initial_condition = surf_conc.initial_condition
+                surf_conc.initialise(
+                    functionspace,
+                    value=initial_condition.value,
+                    label=initial_condition.label,
+                    time_step=initial_condition.time_step,
+                )
+
         # initial guess needs to be non zero if chemical pot
         if self.settings.chemical_pot:
             if self.V.num_sub_spaces() == 0:
@@ -214,7 +227,7 @@ def define_variational_problem(self, materials, mesh, dt=None):
             dt,
             traps=self.traps,
             soret=self.settings.soret,
-            surface_species=self.surface_species,
+            surface_concentrations=self.surface_concentrations,
         )
         F += self.mobile.F
         expressions += self.mobile.sub_expressions
@@ -225,9 +238,9 @@ def define_variational_problem(self, materials, mesh, dt=None):
         expressions += self.traps.sub_expressions
 
         # Add surface species
-        self.surface_species.create_forms(self.mobile, self.T, mesh.ds, dt)
-        F += self.surface_species.F
-        expressions += self.surface_species.sub_expressions
+        self.surface_concentrations.create_forms(self.mobile, self.T, mesh.ds, dt)
+        F += self.surface_concentrations.F
+        expressions += self.surface_concentrations.sub_expressions
 
         self.F = F
         self.expressions = expressions
@@ -333,6 +346,9 @@ def update_post_processing_solutions(self, exports):
         for i, trap in enumerate(self.traps, 1):
             trap.post_processing_solution = res[i]
 
+        for i, surf_conc in enumerate(self.surface_concentrations, len(self.traps) + 1):
+            surf_conc.post_processing_solution = res[i]
+
         if self.settings.chemical_pot:
             self.mobile.post_processing_solution_to_concentration()
         else:
diff --git a/test/simulation/test_postprocessing_integration.py b/test/simulation/test_postprocessing_integration.py
index 91e7dabd1..c35bd0ae2 100644
--- a/test/simulation/test_postprocessing_integration.py
+++ b/test/simulation/test_postprocessing_integration.py
@@ -21,7 +21,9 @@ def my_sim(self):
         trap_1 = festim.Trap(1, 1, 1, 1, mat1, 1)
         my_sim.traps = festim.Traps([trap_1])
 
-        surf_conc1 = festim.SurfaceConcentration(1, 1, 1, 1, 1, 1, 1, 0, 1)
+        surf_conc1 = festim.SurfaceConcentration(
+            1, 1, 1, 1, 1, 1, 1, 0, 1, festim.InitialCondition("adsorbed", 0)
+        )
         my_sim.surface_species = festim.SurfaceConcentrations([surf_conc1])
 
         my_sim.mesh.define_measures(my_sim.materials)